This knowledge of land surface temperature and its spatial variations within a city environment is of prime importance to the study of urban climate and human-environment interactions. Few studies have examined the influence of land use and terrain on the surface temperature effects of semi-arid mountainous urban areas. This study investigates the urban environment characterization and its effects on surface temperature using remote sensing. The methodologies adapted for this study are geometric and radiometric corrections of satellite data, extraction of land use/land cover and digital elevation model, estimation of vegetation density using Normalized Difference Vegetation Index (NDVI), and estimation of surface temperature and emissivity using temperature emissivity separation (TES) algorithm. Finally geospatial model and statistical techniques are used for assessing the overall impact of urban environmental characterization on urban climate of semi-arid region of Abha, Kingdom of Saudi Arabia. Herein, results reveal that the spatial distribution of surface temperature was affected by land use/land cover (LULC) and topography. The high dense built-up and commercial/industrial areas display higher surface temperature in comparison with surrounding lands. There is gradual decrease of LULC classes’ surface temperature with the increase in altitude. The cooling effect towards the surrounding urban built-up area is found increasing at the hill located vegetated area, the downward slope and valley terrain inside the recreational park. Therefore the spatial variation in surface temperature also reflected the effects of topography on LULC classes. Suitable mountainous land use utilization would help to expand the cooling effect. In the future, the outcomes of this study could be used to build environmentally sustainable urban planning suitable to semi-arid regions and to create practices that consider the local weather environment in urban planning.
Land surface temperature is an important factor in global environmental change studies in estimating radiation budgets in heat balance studies and as a control for climate models [
Weng et al. [
Topography is an important influencing factor controlling surface temperature [
Interpreting and analyzing the thermal satellite data and images for thermal pattern over an area is a complex one. In some cases, one must look for pattern of relative temperature differences rather than the absolute values because of the many complex factors that make quantitative determinations complicated; for example number and spatial distribution of different material surfaces in an instantaneous field of view (IFOV). As thermal response depends on composition, density and texture of the materials; vegetation canopy characteristics, including height, leaf geometry, and plant shape, and near surface (1 to 3 meters) air temperature, relative humidity, and wind effects [
The city of Abha is situated in Aseer province in south-western Saudi Arabia. It covers an area of 370 km2. Its boundary lies between the latitude 18˚10'12.39"N and 18˚23'33.05"N and longitude 42˚21'41.58"E and 42˚39'36.09"E. The topography of the area is mountainous with an elevation range between 1951 meters to 2991 meters mean sea level msl. The average annual rainfall is 355 mm, with the bulk of the precipitation occurring between April and August Aseer can experience temperatures ranging from 1.90˚C to 34.80˚C (
The dataset used in the study is given in
ASTER satellite dataset was used in order to effectively identify the spatial distribution characteristics of land cover/land use (LULC) classes and surface temperature for the city of Abha. This study assessed the LULC, normalized difference vegetation index (NDVI) and surface temperature, the spectral radiance value is converted from DN (Digital number) in each pixel by using equation:
2012 | Extreme Min Air Temperature | Extreme Max Air Temperature | Mean Air Temperature | Rainfall in MM |
---|---|---|---|---|
Jan | 1.90 | 25.40 | 14.10 | 0.00 |
Feb | 6.10 | 26.30 | 15.90 | 8.80 |
Mar | 6.50 | 28.40 | 17.20 | 4.60 |
Apr | 12.00 | 28.40 | 20.30 | 21.40 |
May | 13.90 | 33.00 | 21.60 | 34.70 |
Jun | 16.20 | 34.80 | 25.10 | 0.10 |
Jul | 15.00 | 33.00 | 23.30 | 76.90 |
Aug | 13.10 | 32.50 | 23.40 | 29.00 |
Sep | 12.00 | 31.90 | 22.60 | 0.10 |
Oct | 10.60 | 29.30 | 20.00 | 0.90 |
Nov | 4.00 | 26.50 | 15.50 | 8.00 |
Dec | 2.90 | 23.70 | 13.10 | 0.40 |
Subject Area | Data Basis | Source |
---|---|---|
Meteorological Data | Monthly temperature and Rainfall data from Abha stations 2012, Latitude: 18˚14'17.93"N Longitude: 42˚39'13.057"E | Presidency Meteorological Environment KSA |
Satellite Data Used for LULC, Ts and Vegetation Cover | ASTER (7 Nov. 2012) Path/Row = 167/47 - 48 | TERRE/ASTER NASA |
Topographical Data | Contour line, spot height, DEM, Mean slope; mean slope exposure | MOMRA, KSA |
where, UCC is unit conversion coefficients from HDF file.
The unit conversion coefficients that are used for different bands and for different gain settings are given in the ASTER user handbook. Thereafter, the satellite image has been geometrically rectified to a common UTM WGS84 coordinate system.
Considering the objectives of the present study, the following classification scheme used for interpreting land use based on Anderson et al., [
The process of digital elevation model (DEM) creation begins with the scanned, geo-referenced Topographic raster Map (1:50,000). Contour Lines with 20 meters interval, spot elevations, from the raster image are extracted, converted to digital vectors and given elevation values. The Grid-based DEM was generated from the extracted digital contour vector data. The DEM was produced with the “Topo to Raster” interpolation techniques using ArcGIS software. This interpolation technique was specially designed for the creation of hydrologically corrected DEM [
NDVI is the vegetation index used by researchers for extracting vegetation density from remotely sensed data. In essence, the algorithm isolates the dramatic increase in reflectance over the visible red to near infrared wavelengths, and normalizes it by dividing by the overall brightness of each pixel in those wavelengths as shown in the equation:
The values in either band have been converted from raw DN values to reflectance of solar electromagnetic radiation. The result of this algorithm is a single band data, the values ranges from −1 to +1, where values close to +1 signify greater vegetation cover. The NDVI values are estimated in the range of −0.041 to 0.487, having a mean value of 0.113 with a standard deviation of 0.046.
In this study, land surface temperature (LST) was estimated from the thermal infrared bands of ASTER satellite dataset using a temperature emissivity separation (TES) model [
where,
At-sensor radiance data were corrected for atmospheric effects to obtain the radiance emitted by the surface (Lj), using the MODTRAN radiative transfer model. The standard atmospheric parameter (tropical climate) has been used. The output parameters obtained have been used to estimate the radiance using Equations (3) & (4).
where,
Hence,
where,
In the above equation, if the surface emissivity is known, it is possible to correct for the reflected sky radiation. Then the surface temperature may be calculated using Equation (5).
The above equation shows that for radiance measured in “n” spectral channels, there will be “n + 1” unknowns, “n” emissivities and one surface temperature. In TES [
where,
For emissivities between 0.7 - 1.0, the ratios
The maximum-minimum difference between the emissivity ratios
Therefore, the revised emissivity can be computed using the beta
Beta
a mean value of 24.19˚C and standard deviation of 3.63 (
We examined the effects of topography on the spatial distribution of Ts. Jabal al Sooda is the highest point (2981 meters), situated in western part of the study area, whereas urban areas are located on the foot hills of Jabal al Sooda (
LULC Classes | Overall Ts and Area | |||||
---|---|---|---|---|---|---|
MIN ST ˚C | MAX ST ˚C | MEAN ST ˚C | STD | Area in Km | Area in % | |
Built-Up | 12.22 | 33.82 | 25.39 | 2.79 | 34.79 | 9.40 |
Water Bodies | 14.38 | 24.03 | 18.07 | 2.89 | 0.10 | 0.03 |
Agricultural Cropland | 11.24 | 32.54 | 24.61 | 3.48 | 16.29 | 4.40 |
Dense Vegetation | 11.04 | 30.16 | 20.77 | 3.30 | 2.14 | 0.58 |
Sparse Vegetation | 10.68 | 32.90 | 21.04 | 3.59 | 39.63 | 10.71 |
Fallow Land | 13.43 | 33.82 | 25.42 | 3.65 | 31.48 | 8.51 |
Bushes/Scrubland | 11.46 | 32.87 | 22.96 | 3.52 | 40.54 | 10.96 |
Bare Soil/Wasteland | 14.59 | 33.86 | 26.16 | 3.02 | 15.25 | 4.12 |
Rock Outcroplands | 11.39 | 34.58 | 26.48 | 2.85 | 189.78 | 51.29 |
Total | - | - | - | - | 370.00 | 100 |
in the study area i.e. topography, land use, vegetation and wadies, were thought to affect the spatial distribution of Ts.
LULC Classes | Ts (˚C) at Different Elevation Range (in meters) | ||||
---|---|---|---|---|---|
1950-2150 | 2151-2350 | 2351-2550 | 2551-2750 | 2751-2991 | |
Built-Up | 25.23 | 25.31 | 24.25 | 21.69 | 16.29 |
Water Bodies | 18.07 | - | - | - | - |
Agricultural Cropland | 26.98 | 25.80 | 23.41 | 20.64 | 18.93 |
Dense Vegetation | 24.91 | 23.51 | 22.22 | 18.75 | 17.45 |
Sparse Vegetation | 26.24 | 24.70 | 22.02 | 20.07 | 19.24 |
Fallow Land | 27.72 | 26.78 | 24.70 | 22.93 | 20.72 |
Bushes/Scrubland | 27.13 | 25.47 | 23.34 | 21.39 | 20.09 |
Bare Soil/Wasteland | 27.01 | 26.63 | 24.22 | 20.89 | 18.89 |
Rock Outcroplands | 27.80 | 26.70 | 24.42 | 21.34 | 19.58 |
Total Area | 115.96 | 127.48 | 52.12 | 50.92 | 23.52 |
Understanding the mechanism of the surface temperature’s effect of land use is important for urban planning to enable greater control over thermal environments. In this study, land characterization analysis of surface temperature in the semi-arid mountainous city of Abha using remote sensing and GIS has been investigated. For this, Ts information is estimated from ASTER data to analyze the spatial distribution of the LULC effects on Ts. The spatial distribution of Ts was also affected by topography. Ts gradually decreased along the higher altitude land areas. That is, the cooling effect of the topography (hills) prolonged into the urban areas. The result showed that higher altitude regions effectively transfer the cool air to the surrounding low land use areas. Suitable mountainous land use utilization would help to expand the cooling effect. In the future, the results from this study could be used to help inform environmentally sustainable urban planning in semi-arid regions and to create processes that consider the characteristics of local weather environment in planning.
The author wishes to acknowledge the financial support by Deanship of Scientific Research, King Khalid University, KSA. NASA-USGS personnel at the land DAAC provided the latest ASTER-Terra satellite image was also greatly appreciated.